Frailty Modelling for Multivariate Current Status Data with Applications in Epidemiology
Current status data are interval-censored time-to-event data and arise, for example, in infectious disease epidemiology, when blood serum residues are tested for the presence of antibodies to one or more infections. Frailty models are random-effects models for time-to-event data that can be used to quantify the heterogeneity between individuals with respect to factors that are relevant for the transmission of infectious diseases. The aims of the project are to develop new statistical methods for the analysis of multivariate current status data and to evaluate the new methods using both simulated and real data.
This project is funded by the DFG (German Research Foundation).
News and Publications
- Bardo M. (2019): Random-effects modelling of bivariate survival data, Master thesis, University of Göttingen
- Kloidt A.-M. (2019): The statistical analysis of infectious disease data from the PIENTER-2 project, Master thesis, University of Göttingen
Talks and posters
- Bardo M., Hens N., Unkel S. (2021): A family of discrete random effect distributions for modelling bivariate time-to-event data, International Society for Clinical Biostatistics (ISCB) 2021 annual conference, Lyon, France (18-22 July 2021). Oral presentation
- Bardo M., Hens N., Unkel S. (2020): A family of discrete frailty distributions for modelling heterogeneities in the transmission of infectious diseases, GMDS & CEN-IBS 2020 joint conference, Berlin, Germany (6-9 September). Oral presentation (Screencast)
- Unkel S., Kloidt A.-M., Hens N. (2019): A family of discrete frailty distributions for modelling heterogeneities in the transmission of infectious diseases, International Society for Clinical Biostatistics (ISCB) 2019 annual conference, Leuven, Belgium (14-18 July). Poster presentation